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Is Your Public Cloud Data Secure?

Is Your Public Cloud Data Secure?

 

With advancing digitalization, business requirements are also developing rapidly. The rise of cloud applications shows no signs of slowing down. More and more organizations continue to adopt cloud computing at a rapid pace to benefit from increased efficiency, better scalability, and faster deployments. According to a report by Linker, the global public cloud computing market is expected to reach $623.3 billion by 2023. The rapid provision of business applications for the introduction of new, improved business processes is central. Many companies consider outsourcing workloads to the public cloud as a priority. High availability, scalability and cost efficiency open up the possibility of implementing innovative operational developments with little effort.

 

As more workloads are shifting to the cloud, cybersecurity professionals remain concerned about the security of data, systems, and services in the cloud. the public cloud exposes business to a large number of new threats. Its dynamic character makes that relying on traditional security technologies and approaches isn’t enough. Therefore, many companies have to rethink the risk assessment of the data stored in the cloud.

 

While moving their workloads into public cloud, companies think that their business is automatically protected. Unfortunately, this security is not certain. Amazon, Microsoft and Google do indeed partially secure their cloud, but it is not their core business or priority. So, in order to cope with new security challenges, security teams are forced to update their security posture and strategies.

 

A report by RightScale shows that average business runs 38% of workloads in public and 41% in private cloud. Usually, enterprises run a more significant part of their workloads in a private cloud (46%) and a smaller portion (33%) in the public cloud. Small to medium businesses, on the other hand, prefer to use a public cloud (43%), instead of investing in more expensive private solutions (35%).

The cloud computing statistics also show the public cloud spend is growing three times faster than the private cloud usage.

 

For this survey 786 IT professionals were questioned about their adoption of cloud infrastructure and related technologies. 58% of the respondents represented enterprises with more than 1,000 employees. For majority of them, more than 50% of enterprise workloads and data are expected to be in a public cloud within 12 months. More than half of respondents said they’ll consider moving at least some of their sensitive consumer dataor corporate financial data to the cloud.

 

Even tough public cloud adoption continues to accelerate 83% of enterprises indicate that security is a is one of the top challenge, followed by 82% for managing cloud spend and 79% for governance.

Workloads and data in a public cloud for all organizations

 
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Securing the environment in the cloud is one of the biggest challenge or barrier in cloud adoption. If companies want to protect their data in the cloud, they must ensure that the environment is used safely. This requires additional measures at different levels:

 

Secure access with Identity and Access Management (IAM)

 

As the data stored in cloud can be access from any location and any device, access control and whitelisting are among the first and strongest measures to safeguard your cloud. Managing people, roles and identities is primordial in cloud security.

In most companies, user rights for applications, databases, and content are maintained manually in separate access lists. Regulations for dealing with security-relevant technologies are also kept in other places. The lack of automation and distributed access management prevent identity or context attributes that are needed for dynamic Identity and Access Management (IAM) from being considered.

Building an identical repository with clearly defined type of access for each user identity and strict access policies is therefore the first step in the dynamic handling of access rights. For example, it can be specified that employee X login is only permitted from certain geographic locations by secure network connection to access only a selected numbers of files.

While these policies can be managed by different individuals with appropriate authority in the organization, they must exist in a single, reliable, and up-to-date location – across all resources, parameters, and user groups.

 

Data loss prevention (DLP)

 

As data is one of your organization’s most valuable assets, protecting it and keeping it secure must be one of your top priority. In order to accomplish this, a number of DLP controls must be implemented in all cloud applications at various levels and allow IT administrators to intervene. «DLP (Data loss prevention) is the practice of detecting and preventing confidential data from being “leaked” out of an organization’s boundaries for unauthorized use. Data may be physically or logically removed from the organization either intentionally or unintentionally. »

 

Data Encryption

 

Sensitive data may not be transmitted through public networks without adequate encryption. Therefore, one of the most effective cloud security measure that you can take is to encrypt all of your sensitive data in the public cloud. This includes all type of data such as the data at rest inside the cloud and archived and backed-up data, or the data in transit as well. This allows you the complete protection in case of any data exposure, as it remains unreadable and confidential based on your encryption decisions. By encrypting properly data, organizations can also address compliance with government and industry regulations, including GDPR.

HyperScale and Data Management

A hyper-scale data center is mega-sized data centres that have a huge number of computers, network hardware, cooling systems, and are able to support thousands of physical servers and millions of virtual machines. As the technology is growing at exponential rates, big providers such as, Amazon Web Services (AWS), Microsoft Azure, Google, IBM Cloud, Oracle, SAP etc are redefining hyper-scale data centres for data storage and optimising speed to deliver the best software experience possible.

 

In this evolving IT landscape, for customers as well as companies, the question arises of which strategy they want to use for their data storage. They can choose from the following two options, either they can outsource and move their data to an external data center facility, either they can choose to move their traditional ‘physical’ data center to the clouds, means instead of investing in their physical hardware’s, they may opt to rent server in clouds. With this process of outsourcing, companies can reduce payroll costs and ensure that they remain up to date with the latest technological advances.

 

This need of outsource has created the requirement of hyperscale data center, which can operate, analyse and model the massive amounts of data flowing through their systems and offer insights into user behaviour that can be used to generate further income streams. As traditional architectures are not suited to adapt to new cloud centric infrastructure and operations, outsourcing Hyperscale represents a lot of advantages. Companies can benefit from a flexible and customizable IT infrastructure at exactly the level of traceability and cost, adapted to their capacity and need for specific workloads. The principle is simple: The low Total Cost of Ownership (TCO) comes from the fact that the data centers are usually equipped with standard and thus inexpensive standard components and second, that due to virtual infrastructures for larger data volume no more space, air conditioning or electricity is needed.

 

However, companies must not only work with one a single vendor. Using cloud services from more than one hyperscaler avoids dependency on a single vendor. Connecting with different Hyperscaler companies can also choose, depending on the requirements, the individually matching cloud service. This keeps them flexible in order to respond quickly and cost-effectively to new business challenges. This approach requires well-thought-out architectures, as in addition to disk space and computing capacity, data traffic also costs money in the cloud. Unnecessary and duplicate data exchange with Second Source can also increase costs.

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But where exactly are the differences in the hyperscalers? Well, the pitfalls lie in the different cloudstack. The most important distinguishing features can be divided into three categories: product diversity, performance classes and workload-specific target groups.

As no company is the same, sometimes performance, sometimes security, sometimes compliance, then again the availability or the costs or criteria such as scalability, connectivity and other workloads have priority. This question must respond to each companie’s need individually in the selection of hyperscaler.

 

Both in strategy and in purchasing, new thinking is required. Because the potential of the Hyperscaler can only be raised if the companies say goodbye to their single sourcing strategy. Multi-cloud sourcing strategies imply that businesses can move from one provider to another at any time and even distribute the same workloads among multiple resources.

 

Public cloud services are now demanded not only by large but also by medium-sized companies as a managed service. Hybrid and multi-cloud models, which constantly analyze new services and integrate them into managed service offerings, dominate.

For the path of the public cloud transformation, the supporting service providers have to offer a wide range of services and technical implementation. They act as partners of the large public cloud providers and have to know their advantages and disadvantages and advise their customers accordingly. Service Providers need to know all the offerings of the cloud providers in detail and to harmonize them with the requirements and business processes of the user companies.

 

Hyperscale computing is highly decisive choice for organizations dealing with large data volume, so the usage of the new computing system will be seen more in the area where companies demand big analytical needs.Since hyperscale computing is attracting more users, legacy methods of data management are no longer enough to meet the data management needs due to explosive data growth. HyperScale Technology provides a modern data management that is scalable, highly resilient, and simple. It allows organizations to manage data seamlessly on-premises and in the cloud. With Hyperscale Technology solutions companies can removes the burden of day-to-day operations with a simplified installation, automated and self-service operations, and update process.

AI IN CUSTOMER COMMUNICATION – 5 PRELIMINARY QUESTIONS

 

AI is set to be a game-changer for businesses across every industry. Artificial intelligence is undoubtedly changing the way companies address and interact with their customers.  Pluq, the increasing adoption of digital language assistants such as Alexa and Co., Siri and Amazon Echo, in private households is leading-edge.

 

A new study by Bitkom and Deloitte on the future of consumer technology showed that, in addition to 13%, who already had an intelligent virtual assistantin 2018, 4% of those surveyed are planning a purchase a voice assistant in 2019 and 27% can imagine controlling devices by voice in the future. The fact that, according to Gartner, 30% of companies will use AI for at least one key sales process in 2020, which encourages AI adaptation. Companies are faced with the huge task of adapting to the increasingly complex communication needs of their customers. Therefore, language assistants are being integrated into more and more devices.

 

Study highlighted the rapid rise of intelligent language assistants in 2018 and in the coming years we will control more and more devices with our voice. Which opens gates to a new billion-dollar market.

 

Despite all the forward-looking tips and statistics, many companies are still wondering how they can ideally use AI for themselves. Also, the costs that result, the impact on employees and customer satisfaction is not really measurable for many.

To get an overview here you should ask the following basic questions:

 

  1. What do customers really want?

Often, when answering this question, it helps to have a closer look at the customer database. Age structure, nature and complexity of incoming requests provide a clear direction. For example, an airline can quickly and efficiently handle the query of travel times with the help of artificial intelligence. But customers still preferer a human contact when questions about insurance details or specific health problems rise.

 

  1. How is automation currently being used?

Automation is not just a topic for companies since the introduction of artificial intelligence. Many have already integrated automatic systems such as IVR (Interactive Voice Response) for telephone inquiries and automated e-mails or SMS into customer communication – systems that have proven themselves so far. Implementing artificial intelligence here is not necessarily the way to go. Rather, one should analyze how existing systems can be improved to meet evolving customer needs. For example, an automated language solution with machine learning in the background could complement an existing solution and offer the customer an improved contact experience.

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  1. How will the employees react?

The biggest fear among employees is that artificial intelligence makes them redundant in the foreseeable future, for example through chatbots, and as a result they lose their jobs. The fear since the beginning of the industrial age, that machines will take over humans and jobs, is the biggest communications challenge. This is further aggravated by; a lack of understanding of AI compounded by confusing communication by various players during the current hype cycle. There is a need for constant communication and increasing awareness to improve the understanding and applications of AI. One just needs to look at history to conclude that every technological change created an explosion of new jobs and services and, overall, generated more wealth for all.

 

  1. How to find the right AI solution and how should an implementation work?

There are already a variety of AI solutions for various functions, including, for example, Natural Language Processing (NPL). You should basically get an overview of the solution providers – especially those who have a platform with interfaces to different AI solutions in their portfolio. It is essential, however, that there is a precise idea of ​​the existing communication infrastructure and the improvements to be achieved in customer communication. For example, cloud-enabled contact center vendors and specialized integration offerings with an end-to-end AI package can bridge the gap between existing functionality and the AI ​​skills needed to meet existing needs.

 

  1. How is one prepared for the future?

AI will inevitably play a major role in the future of customer contact. But there are many details to consider when planning implementation – even though the customer base is not yet fully receptive to this technology, the rapid development of AI and the ability to address more complex issues can lead to that acceptance which will increase significantly in just a few years. Also, the increasing adaptation of consumers to these types of interfaces will increase their acceptance to, and expectation of, this technology. Long-term planning should therefore always leave room to introduce new innovations as soon as they can offer defined added value.

 

This is precisely why Cloud-based contact center and integration technologies are available that are inherently capable of adapting flexibly to new developments and introducing new third-party connectors. This open technology has the advantage of reducing the risks for future AI and contact center planning and provides the ability to introduce functionality as needed. This avoids being late for a new innovation and losing valuable competitive advantages.

 

Google reveals five security issues concerning Artificial intelligence

In a recent article published by Google, they’ve revealed five major security problems related to Artificial Intelligence. From now on, companies will have to follow a guide on their future Al system to control robots before they can interact with humans.

 

The artificial intelligence is designed to mimic the human brain, or at least its logic when it comes to making decisions. Before worrying about whether an artificial intelligence (AI) could become so powerful that can dominate humans, it would be better to make sure that robots (also called our future colleagues and household companions) are trustworthy. That’s what Google has tried to explain to us. Google’s artificial intelligence specialists have worked with researchers from the Universities of Stanford and Berkeley (California, USA) and with the Association OpenAI on concrete security issues that we must work to resolve.

 

In white paper titled “Concrete Problems in AI Safety” this team describes five “practical problems” of accidents in artificial intelligence-based machine could cause if they aren’t designed properly. Al specialists define accidents as “unexpected and harmful behavior that may emerge from poor design of real world machine learning systems”. In short, these are not potential errors of robots we should be feared but those of their designers.

 

To concretely illustrate their point of view, the authors of this study voluntarily took a random example of a “cleaning robot”. However, it’s quite clear that the issues apply to all forms of AI controlling a robot.

 

 

Pour prévenir ce cas de figure, la solution pourrait consister à créer des « contraintes de bon sens » sous la forme de pénalités infligées à l’IA lorsqu’elle cause une perturbation majeure à l’environnement dans lequel le robot évolue.


  • A robot may disrupt the environment :

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The first two risks identified by the researchers from Google and their acolytes are related to a poor coordination and allocation of the main objective. There is first what they call “Avoiding Negative Side Effects”. Specifically, how to avoid environment related problems caused by a robot while it’s accomplishing its mission. For example, the cleaner could well topple or crush what is on his way because he calculated the fastest route to complete its task. To prevent this scenario, the solution may be to create “constraints of common sense” in the form of penalties imposed on the IA when he causes a major disruption to the environment in which the robot moves.


  • The machine can cheat :  

Second risk of Al based machines is to avoiding reward hacking. For IA, the reward is the success of the goal. Avoid the quest reward from turning into a game and the machine trying to get by all means, even skip steps or cheat. In the case of cleaning robot, it would for example to hide the dirt under the rug in order to say “that’s it, I’m done.”
A difficult problem to solve as an Al can be interpreted in many different ways a task and the environment it meets. One of the ideas in the article is to truncate the information so that the program does not have a perfect knowledge of how to get a reward and thus does not seek to go shorter or easier.


  • How to setup the robot go to the basics?

The third risk is called scalable oversight. More the goal is complex, AI will have to validate his progress with his human referent, which would quickly become tiresome and unproductive. How to proceed so the robot can accomplish itself certain stages of its mission to be effective while knowing seek approval in situations that he will know how to interpret? Example: tidy and clean the kitchen, but ask what to do in the saucepan on the stove. It would simplify to the maximum step of the cooking task so that the robot goes to the point without coming to disturb you during your nap every time.


  • How much independence can you give to an AI?

The next identified problem is the safe exploration of Al. How much independence can you give an AI? The whole point of artificial intelligence is that it can make progress by experimenting different approaches to evaluate the results and decide to keep the most relevant scenarios to achieve its objective. Thus, Google says, if our brave robot would be well advised to learn to perfect its handling of the sponge, we wouldn’t want it to clean an electrical outlet! The suggested solution is to train these Al with simulated environment in which their empirical experiments will not create any risk of accident.


  • Does AI will adapt the change?

Fifth and final problem: robustness to distributional shift or how to adapt to change. “How to be ensured that AI recognizes and behaves properly when it is in a very different environment from the one in which it was being driven? It is clear that we wouldn’t want the robot who was trained to wash the floor of a factory with detergent products does not apply the same technique if asked to clean home.

The article ends by saying that these problems are relatively easy to overcome with the technical means currently available but it’s better to be prudent and develop security policies that can remain effective as the autonomous systems will gain in power. Google is also working on an “emergency stop” button for all menacing AI, if eventually one or several of these risks were not fully mastered,

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